Measurement of blood flow in the heart and vessels using the Doppler effect is well known. Whereas the amplitude of the reflected waves is employed to produce black and white images of the tissues, the frequency shift of the reflected waves may be used to measure the velocity of reflecting scatterers from tissue or blood. Color flow images are produced by superimposing a color image of the velocity of moving material, such as blood, over the black and white anatomical image. The measured velocity of flow at each pixel determines its color.
A major difficulty in making Doppler effect measurements of reflected ultrasonic waves from blood is that the received echo signal typically contains a large component produced by stationary or slowly moving tissues, whereas blood reflects ultrasound very weakly. The stationary tissues do not produce any frequency shift in the reflected waves and these components can easily be filtered out without affecting the flow measurement. However, the reflections produced by the moving tissue due to cardiac or respiratory motion are frequency shifted and may completely overwhelm signals from slowly flowing blood.
In standard color flow processing, a high pass filter known as a wall filter is applied to the data before a color flow estimate is made. The purpose of this filter is to remove signal components produced by tissue surrounding the blood flow of interest. If these signal components are not removed, the resulting velocity estimate will be a combination of the velocities from the blood flow and the surrounding tissue. The backscatter component from tissue is many times larger than that from blood, so the velocity estimate will most likely be more representative of the tissue, rather than the blood flow. In order to get the flow velocity, the tissue signal must be filtered out.
Most commonly, color flow processors assume that the large signal returning from the surrounding tissue is static, that is the tissue is not moving. If this is the case, the quadrature I and Q data can be filtered separately with simple real filters which remove the DC component. The cutoff frequency of these high pass filters can be varied for a given application by changing the filter coefficients.
The assumption of static tissue is generally a good one for radiology applications, except in the abdomen, where residual respiratory and cardiac motion cause some amount of tissue motion. In addition, the motion of the handheld transducer will also look like tissue motion. Since the velocity of this motion is usually slow compared to the velocity of the blood flow being imaged, the operator can set the wall filter cutoff frequency high enough to filter out the tissue signal component. Filtering in this way, however, will also remove signals from low-velocity blood flow, which are often the signals that the operator wants to image.
Two methods to solve this problem have already been proposed. In both of these methods, the velocity of the moving tissue is measured and then the non-DC wall signal is intelligently filtered out. In the first method, a spectral estimate is made using the unfiltered data, and the appropriate spectral components are "excised" prior to the flow mean frequency estimate. In the second method, the mean wall velocity is calculated from the unfiltered data, the complex I, Q data for each firing adjusted such that the resulting wall signal appears to be centered at DC, and then simple DC wall filters are used. A third approach is to filter the quadrature I, Q data as a complex signal with a complex filter, which will allow using appropriate filters whose stop bands are not centered around DC. In this case, the filter coefficients would be dynamically adapted using the measured wall velocity estimate.
All of the foregoing methods require that the wall velocity can be estimated accurately, which is true if the vessels being imaged are relatively small such that there is a very large tissue to blood backscatter ratio in the range cell. For a large vessel however, range cells inside the vessel will have flow signals that are equal to or larger than the wall component, and the unfiltered data will not accurately represent the wall velocity. If these cases are processed adaptively, a portion or all of the flow signal will be treated as a wall signal and filtered out. For this reason, these cases must be identified and the adaptive processing turned off.